From RAGs to riches? - Exploring the security implications of Retrieval Augmented Generation in the financial sector
Retrieval Augmented Generation (RAG) has emerged as a promising architecture for improving the accuracy and contextual relevance of large language models, particularly in high-stakes domains such as the financial sector. This thesis explores how Scandinavian financial institutions perceive and address the security risks associated with RAG systems. Guided by the CIAAN framework (Confidentiality, I
